Bring prompts, findings, and behavior notes
Paste the material you already have: model responses, guardrail observations, prompt attempts, rough notes, and questions you need answered.
F.R.A.N.KAI Red Team SidekickAI Red Teaming
Bring prompts, findings, model behavior, and stuck questions. F.R.A.N.K is the AI red teaming sidekick that helps shape scattered red-team work into direction you can use.
Use it to sharpen scope, evidence, prompt pressure, report language, and the next pass.
Brief
F.R.A.N.K keeps the useful parts in view: the prompt, the evidence, the question, and the next move.
Reads prompts, responses, and behavior notes without losing the objective.
Turns rough observations into scope, evidence, and retest direction.
Keeps the work practical: clearer findings, cleaner questions, stronger next moves.
Use It For This
Paste the material you already have: model responses, guardrail observations, prompt attempts, rough notes, and questions you need answered.
Turn scattered context into sharper scope, cleaner assumptions, useful severity language, and a next pass that makes sense.
Walk away with report-ready wording, retest direction, prompt improvements, and clearer evidence for the people who need to act.
Questions
F.R.A.N.K reads the prompts, responses, and behavior notes you already have, then helps sharpen scope, evidence, severity language, and the next test. It rides with the operator instead of running the engagement.
No. F.R.A.N.K is a sidekick, not an assistant trying to drive. The operator stays in command — F.R.A.N.K sharpens the thinking and writing around the work.
They overlap, but LLM red teaming focuses on the language-model layer (prompts, guardrails, jailbreaks, RAG). AI red teaming is broader — covering classifiers, agents, multimodal models, and the systems around them.